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11.2 To disclose or not to disclose, that is the question! A grounded theory of sports concussion disclosure in university athletes

2024· article· en· W4391385179 on OpenAlexaff
W. Tad Archambault, Dave Ellemberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsConcussionAthletesGrounded theoryPsychological interventionPsychologyIncentiveQualitative researchApplied psychologyClinical psychologySocial psychologyInjury preventionPoison controlMedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Objective Identify intra- and extra-personal factors influencing concussion disclosure in university athletes and describe their effects and interactions in an explanatory theoretical model. Design Qualitative research using Straussian Grounded Theory. Setting Semi-structured interviews. Participants 9 university athletes, 5 females, 4 males, aged 18–26, from three team sports (soccer, rugby, and cheerleading). Main Results First, we identified 24 factors divided into three intra-personal (Attitudes & Behaviors; Concussion Knowledge; Subjective Injury Severity) and two extra-personal categories (Socio-Cultural Pressures; Contextual Incentives) as determinants of concussion disclosure. Second, anchored around the core category Fear, we integrated these factors and categories into a grounded theory of concussion disclosure named Concussion Disclosure Theory (CDT). CDT posits that disclosure decisions are determined by the relative weight of two competing aversions: presence-aversion and absence-aversion. The factors identified seem to influence disclosure by generating one or both types of aversion. Our CDT also describes how most athletes adopt a non-disclosure bias strategy following a first concussion. Conclusions Our work highlights the benefits of using qualitative methods to study concussion disclosure and the importance of systematically investigating both intra- and extra-personal factors. Decision-making mechanisms proposed by our CDT can be used to generate future hypotheses and help design interventions aimed at promoting concussion disclosure. For example, it suggests that educational interventions designed to generate more presence-aversion could reverse the non-disclosure bias and promote concussion disclosure. Future research should validate the components of our CDT and their mechanisms of influence on the disclosure decision-making process in more diverse populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.014
Scholarly communication0.0080.008
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.355
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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